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Abstract

Developmental cognitive neuroscience is being pulled in new directions by network science and big data. Brain imaging [e.g., functional magnetic resonance imaging (fMRI), functional connectivity MRI], analytical advances (e.g., graph theory, machine learning), and access to large computing resources have empowered us to collect and process neurobehavioral datafaster and in larger populations than ever before. The translational potential from these advances is unparalleled, as a better understanding of complex human brain functions is best grounded in the onset of these functions during human development. However, the maturation of developmental cognitive neuroscience has seen the emergence of new challenges and pitfalls, which have significantly slowed progress and need to be overcome to maintain momentum. In this review, we examine the state of developmental cognitive neuroscience in the era of networks and big data. In addition, we provide a discussion of the strengths, weaknesses, opportunities, and threats (SWOT) of the field to advance developmental cognitive neuroscience's scientific and translational potential.

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2021-12-09
2024-04-30
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Literature Cited

  1. Abraham A, Milham MP, Di Martino A, Craddock RC, Samaras D et al. 2017. Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147:736–45
    [Google Scholar]
  2. ADHD-200 Consort 2012. The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6:62
    [Google Scholar]
  3. Akil H, Balice-Gordon R, Cardozo DL, Koroshetz W, Posey Norris SM et al. 2016. Neuroscience training for the 21st century. Neuron 90:5917–26
    [Google Scholar]
  4. Alcauter S, Lin W, Smith JK, Goldman BD, Reznick JS et al. 2015. Frequency of spontaneous BOLD signal shifts during infancy and correlates with cognitive performance. Dev. Cogn. Neurosci. 12:40–50
    [Google Scholar]
  5. Alexander LM, Escalera J, Ai L, Andreotti C, Febre K et al. 2017. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4:170181
    [Google Scholar]
  6. Alexander LM, Salum GA, Swanson JM, Milham MP. 2020. Measuring strengths and weaknesses in dimensional psychiatry. J. Child Psychol. Psychiatry 61:140–50
    [Google Scholar]
  7. All of Us Res. Program Investig 2019. The “All of Us” Research Program. N. Engl. J. Med 381:668–76
    [Google Scholar]
  8. Anderson JS, Nielsen JA, Ferguson MA. 2014. Utah 2 - University of Utah (Anderson, Nielsen) Consortium for Reliability and Reproducibility. http://fcon_1000.projects.nitrc.org/indi/CoRR/html/utah_2.html .
  9. Ashley EA. 2015. The precision medicine initiative: a new national effort. JAMA 313:212119–20
    [Google Scholar]
  10. Assaf Y, Bouznach A, Zomet O, Marom A, Yovel Y 2020. Conservation of brain connectivity and wiring across the mammalian class. Nat. Neurosci. 23:7805–8
    [Google Scholar]
  11. Baller EB, Kaczkurkin AN, Sotiras A, Adebimpe A, Bassett DS et al. 2021. Neurocognitive and functional heterogeneity in depressed youth. Neuropsychopharmacology 46:4783–90
    [Google Scholar]
  12. Barnes LL, Bennett DA. 2014. Alzheimer's disease in African Americans: risk factors and challenges for the future. Health Aff 33:4580–86
    [Google Scholar]
  13. Bassett DS, Xia CH, Satterthwaite TD. 2018. Understanding the emergence of neuropsychiatric disorders with network neuroscience. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3:9742–53
    [Google Scholar]
  14. Bertolero MA, Dworkin JD, David SU, Lloreda CL, Srivastava P et al. 2020. Racial and ethnic imbalance in neuroscience reference lists and intersections with gender. bioRxiv 2020.10.12.336230. https://doi.org/10.1101/2020.10.12.336230
    [Crossref]
  15. Bijsterbosch J, Harrison SJ, Jbabdi S, Woolrich M, Beckmann C et al. 2020. Challenges and future directions for representations of functional brain organization. Nat. Neurosci. 23:121484–95
    [Google Scholar]
  16. Biswal BB, Mennes M, Zuo X-N, Gohel S, Kelly C et al. 2010. Toward discovery science of human brain function. PNAS 107:104734–39
    [Google Scholar]
  17. Biswal BB, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med 34:4537–41
    [Google Scholar]
  18. Boekel W, Wagenmakers E-J, Belay L, Verhagen J, Brown S, Forstmann BU. 2015. A purely confirmatory replication study of structural brain-behavior correlations. Cortex 66:115–33
    [Google Scholar]
  19. Botvinik-Nezer R, Holzmeister F, Camerer CF, Dreber A, Huber J et al. 2020. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582:781084–88
    [Google Scholar]
  20. Braga RM, Buckner RL. 2017. Parallel interdigitated distributed networks within the individual estimated by intrinsic functional connectivity. Neuron 95:457–71
    [Google Scholar]
  21. Bullmore E, Sporns O. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10:3186–98
    [Google Scholar]
  22. Bunge SA, Wright SB. 2007. Neurodevelopmental changes in working memory and cognitive control. Curr. Opin. Neurobiol. 17:2243–50
    [Google Scholar]
  23. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J et al. 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14:5365–76
    [Google Scholar]
  24. Byrd JB, Greene AC, Prasad DV, Jiang X, Greene CS. 2020. Responsible, practical genomic data sharing that accelerates research. Nat. Rev. Genet. 21:10615–29
    [Google Scholar]
  25. Callaghan B, Meyer H, Opendak M, Van Tieghem M, Harmon C et al. 2019. Using a developmental ecology framework to align fear neurobiology across species. Annu. Rev. Clin. Psychol. 15:345–69
    [Google Scholar]
  26. Casey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM et al. 2018. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32:43–54
    [Google Scholar]
  27. Casey BJ, Cohen JD, Jezzard P, Turner R, Noll DC et al. 1995. Activation of prefrontal cortex in children during a nonspatial working memory task with functional MRI. NeuroImage 2:3221–29
    [Google Scholar]
  28. Chabernaud C, Mennes M, Kelly C, Nooner K, Di Martino A et al. 2012. Dimensional brain-behavior relationships in children with attention-deficit/hyperactivity disorder. Biol. Psychiatry 71:5434–42
    [Google Scholar]
  29. Choudhury S, Fishman JR, McGowan ML, Juengst ET. 2014. Big data, open science and the brain: lessons learned from genomics. Front. Hum. Neurosci 8:239
    [Google Scholar]
  30. Chuard PJC, Vrtílek M, Head ML, Jennions MD. 2019. Evidence that nonsignificant results are sometimes preferred: reverse P-hacking or selective reporting?. PLOS Biol 17:1e3000127
    [Google Scholar]
  31. Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE. 2014. Intrinsic and task-evoked network architectures of the human brain. Neuron 83:1238–51
    [Google Scholar]
  32. Corbetta M, Shulman GL. 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3:3201–15
    [Google Scholar]
  33. Couzin-Frankel J. 2019. New effort aims to study brain diseases in African-Americans. Science Mar. 22. https://doi.org/10.1126/science.aax4355
    [Crossref] [Google Scholar]
  34. Cui Z, Li H, Xia CH, Larsen B, Adebimpe A et al. 2020. Individual variation in functional topography of association networks in youth. Neuron 106:2340–53.e8
    [Google Scholar]
  35. Cutler DM. 2020. Early returns from the era of precision medicine. JAMA 323:2109–10
    [Google Scholar]
  36. Damaraju E, Caprihan A, Lowe JR, Allen EA, Calhoun VD, Phillips JP 2014. Functional connectivity in the developing brain: a longitudinal study from 4 to 9 months of age. NeuroImage 84:169–80
    [Google Scholar]
  37. Data sharing and the future of science 2018. Nat. Commun. 9:12817
  38. Davidson MC, Thomas KM, Casey BJ 2003. Imaging the developing brain with fMRI. Ment. Retard. Dev. Disabil. Res. Rev. 9:3161–67
    [Google Scholar]
  39. De Asis-Cruz J, Bouyssi-Kobar M, Evangelou I, Vezina G, Limperopoulos C. 2015. Functional properties of resting state networks in healthy full-term newborns. Sci. Rep. 5:17755
    [Google Scholar]
  40. de Bie HMA, Boersma M, Adriaanse S, Veltman DJ, Wink AM et al. 2012. Resting-state networks in awake five- to eight-year old children. Hum. Brain Mapp. 33:51189–201
    [Google Scholar]
  41. Di Martino A, O'Connor D, Chen B, Alaerts K, Anderson JS et al. 2017. Enhancing studies of the connectome in autism using the Autism Brain Imaging Data Exchange II. Sci. Data 4:170010
    [Google Scholar]
  42. Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. 2008. A dual-networks architecture of top-down control. Trends Cogn. Sci. 12:399–105
    [Google Scholar]
  43. Dosenbach NUF, Fair DA, Miezin FM, Cohen AL, Wenger KK et al. 2007. Distinct brain networks for adaptive and stable task control in humans. PNAS 104:2611073–78
    [Google Scholar]
  44. Dosenbach NUF, Koller JM, Earl EA, Miranda-Dominguez O, Klein RL et al. 2017. Real-time motion analytics during brain MRI improve data quality and reduce costs. NeuroImage 161:80–93
    [Google Scholar]
  45. Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power JD et al. 2010. Prediction of individual brain maturity using fMRI. Science 329:59971358–61
    [Google Scholar]
  46. Dosenbach NUF, Visscher KM, Palmer ED, Miezin FM, Wenger KK et al. 2006. A core system for the implementation of task sets. Neuron 50:5799–812
    [Google Scholar]
  47. Duchesne S, Badhwar A, Lussier-Levesque D. 2021. SIMON dataset Medics Lab., Univ. Laval, Quebec City accessed June 7, 2021. http://fcon_1000.projects.nitrc.org/indi/retro/SIMON.html
  48. Eggebrecht AT, Elison JT, Feczko E, Todorov A, Wolff JJ et al. 2017. Joint attention and brain functional connectivity in infants and toddlers. Cereb. Cortex 27:31709–20
    [Google Scholar]
  49. Elliott ML, Knodt AR, Hariri AR. 2021. Striving toward translation: strategies for reliable fMRI measurement. Trends Cogn. Sci. 25:776–87
    [Google Scholar]
  50. Eyre M, Fitzgibbon SP, Ciarrusta J, Cordero-Grande L, Price AN et al. 2021. The developing human connectome project: typical and disrupted perinatal functional connectivity. Brain 144:72199–213
    [Google Scholar]
  51. Fair DA. 2018. The big reveal: Precision mapping shines a gigantic floodlight on the cerebellum. Neuron 100:4773–76
    [Google Scholar]
  52. Fair DA, Cohen AL, Dosenbach NUF, Church JA, Miezin FM et al. 2008. The maturing architecture of the brain's default network. PNAS 105:104028–32
    [Google Scholar]
  53. Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA et al. 2009. Functional brain networks develop from a ‘local to distributed’ organization. PLOS Comput. Biol. 5:5e1000381
    [Google Scholar]
  54. Fair DA, Miranda-Dominguez O, Snyder AZ, Perrone A, Earl EA et al. 2020. Correction of respiratory artifacts in MRI head motion estimates. NeuroImage 208:116400
    [Google Scholar]
  55. Fair DA, Nigg JT, Iyer S, Bathula D, Mills KL et al. 2012. Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Front. Syst. Neurosci. 6:80
    [Google Scholar]
  56. Feczko E, Conan G, Marek S, Tervo-Clemmens B, Cordova M et al. 2021. Adolescent Brain Cognitive Development (ABCD) community MRI collection and utilities. bioRxiv 2021.07.09451638. https://doi.org/10.1101/2021.07.09.451638
    [Crossref]
  57. Feczko E, Fair DA 2020. Methods and challenges for assessing heterogeneity. Biol. Psychiatry 88:19–17
    [Google Scholar]
  58. Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. 2019. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn. Sci. 23:7584–601
    [Google Scholar]
  59. Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J et al. 2015. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18:1664–71
    [Google Scholar]
  60. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME. 2006. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. PNAS 103:2610046–51
    [Google Scholar]
  61. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. PNAS 102:279673–78
    [Google Scholar]
  62. Fox PT, Mintun MA, Reiman EM, Raichle ME. 1988. Enhanced detection of focal brain responses using intersubject averaging and change-distribution analysis of subtracted PET images. J. Cereb. Blood Flow Metab. 8:5642–53
    [Google Scholar]
  63. Fransson P. 2005. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum. Brain Mapp 26:115–29
    [Google Scholar]
  64. Fransson P, Skiöld B, Horsch S, Nordell A, Blennow M et al. 2007. Resting-state networks in the infant brain. PNAS 104:3915531–36
    [Google Scholar]
  65. Gao W, Alcauter S, Elton A, Hernandez-Castillo CR, Smith JK et al. 2015a. Functional network development during the first year: relative sequence and socioeconomic correlations. Cereb. Cortex 25:92919–28
    [Google Scholar]
  66. Gao W, Alcauter S, Smith JK, Gilmore JH, Lin W. 2015b. Development of human brain cortical network architecture during infancy. Brain Struct. Funct. 220:21173–86
    [Google Scholar]
  67. Gao W, Lin W, Grewen K, Gilmore JH 2017. Functional connectivity of the infant human brain: plastic and modifiable. Neuroscientist 23:2169–84
    [Google Scholar]
  68. Gazzaniga MS. 2014. Handbook of Cognitive Neuroscience Boston: Springer
  69. Gazzaniga MS, Ivry RB, Mangun GR. 2019. The Cognitive Neurosciences Cambridge, MA: MIT Press, 5th ed..
  70. Geng X, Li G, Lu Z, Gao W, Wang L et al. 2017. Structural and maturational covariance in early childhood brain development. Cereb. Cortex 27:31795–807
    [Google Scholar]
  71. Genon S, Wensing T, Reid A, Hoffstaedter F, Caspers S et al. 2017. Searching for behavior relating to grey matter volume in a-priori defined right dorsal premotor regions: lessons learned. NeuroImage 157:144–56
    [Google Scholar]
  72. Gilmore JH, Shi F, Woolson SL, Knickmeyer RC, Short SJ et al. 2012. Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22:112478–85
    [Google Scholar]
  73. Gilmore-Bykovskyi AL, Jin Y, Gleason C, Flowers-Benton S, Block LM et al. 2019. Recruitment and retention of underrepresented populations in Alzheimer's disease research: a systematic review. Alzheimer's Dement 5:751–70
    [Google Scholar]
  74. Goldman MS, Fee MS. 2017. Computational training for the next generation of neuroscientists. Curr. Opin. Neurobiol. 46:25–30
    [Google Scholar]
  75. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ et al. 2017. Precision functional mapping of individual human brains. Neuron 95:4791–807.e7
    [Google Scholar]
  76. Gordon EM, Laumann TO, Marek S, Newbold DJ, Hampton JM et al. 2021. Human fronto-striatal connectivity is organized into discrete functional subnetworks. bioRxiv 2021.04.12.439415. https://www.biorxiv.org/content/10.1101/2021.04.12.439415
  77. Gordon EM, Laumann TO, Marek S, Raut RV, Gratton C et al. 2020. Default-mode network streams for coupling to language and control systems. PNAS 117:2917308–19
    [Google Scholar]
  78. Gould J. 2020. The career costs of COVID-19: How postdocs and PhD students are paying the price. Nature Nov. 25. https://doi.org/10.1038/d41586-020-03108-4
    [Crossref] [Google Scholar]
  79. Gozzi A, Schwarz AJ. 2016. Large-scale functional connectivity networks in the rodent brain. NeuroImage 127:496–509
    [Google Scholar]
  80. Graham AM, Pfeifer JH, Fisher PA, Lin W, Gao W, Fair DA 2015. The potential of infant fMRI research and the study of early life stress as a promising exemplar. Dev. Cogn. Neurosci. 12:12–39
    [Google Scholar]
  81. Gratton C, Kraus BT, Greene DJ, Gordon EM, Laumann TO et al. 2020. Defining individual-specific functional neuroanatomy for precision psychiatry. Biol. Psychiatry 88:128–39
    [Google Scholar]
  82. Gratton C, Laumann TO, Nielsen AN, Greene DJ, Gordon EM et al. 2018. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98:2439–52.e5
    [Google Scholar]
  83. Grayson DS, Bliss-Moreau E, Machado CJ, Bennett J, Shen K et al. 2016. The rhesus monkey connectome predicts disrupted functional networks resulting from pharmacogenetic inactivation of the amygdala. Neuron 91:2453–66
    [Google Scholar]
  84. Grayson DS, Fair DA. 2017. Development of large-scale functional networks from birth to adulthood: a guide to the neuroimaging literature. NeuroImage 160:15–31
    [Google Scholar]
  85. Green-Harris G, Coley SL, Koscik RL, Norris NC, Houston SL et al. 2019. Addressing disparities in Alzheimer's disease and African-American participation in research: an asset-based community development approach. Front. Aging Neurosci. 11:125
    [Google Scholar]
  86. Greene DJ, Koller JM, Hampton JM, Wesevich V, Van AN et al. 2018. Behavioral interventions for reducing head motion during MRI scans in children. NeuroImage 171:234–45
    [Google Scholar]
  87. Greene DJ, Marek S, Gordon EM, Siegel JS, Gratton C et al. 2020. Integrative and network-specific connectivity of the basal ganglia and thalamus defined in individuals. Neuron 105:4742–58.e6
    [Google Scholar]
  88. Greicius MD, Krasnow B, Reiss AL, Menon V. 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. PNAS 100:1253–58
    [Google Scholar]
  89. Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G et al. 2014. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95:232–47
    [Google Scholar]
  90. Gu S, Satterthwaite TD, Medaglia JD, Yang M, Gur RE et al. 2015. Emergence of system roles in normative neurodevelopment. PNAS 112:4413681–86
    [Google Scholar]
  91. Hallquist MN, Hwang K, Luna B. 2013. The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage 82:208–25
    [Google Scholar]
  92. Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD. 2015. The extent and consequences of p-hacking in science. PLOS Biol 13:3e1002106
    [Google Scholar]
  93. Hoffman EA, Howlett KD, Breslin F, Dowling GJ. 2018. Outreach and innovation: communication strategies for the ABCD study. Dev. Cogn. Neurosci. 32:138–42
    [Google Scholar]
  94. Huttenlocher PR. 2009. Neural Plasticity Cambridge, MA: Harvard Univ. Press
  95. Innocenti GM, Price DJ. 2005. Exuberance in the development of cortical networks. Nat. Rev. Neurosci. 6:12955–65
    [Google Scholar]
  96. Ioannidis JPA. 2005. Why most published research findings are false. PLOS Med 2:8e124
    [Google Scholar]
  97. Jalbrzikowski M, Liu F, Foran W, Klei L, Calabro FJ et al. 2020. Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart. Hum. Brain Mapp.154187–99
    [Google Scholar]
  98. Jo HJ, Gotts SJ, Reynolds RC, Bandettini PA, Martin A et al. 2013. Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. J. Appl. Math. 2013.935154
    [Google Scholar]
  99. Jones-London M. 2020. NINDS strategies for enhancing the diversity of neuroscience researchers. Neuron 107:2212–14
    [Google Scholar]
  100. Kaczkurkin AN, Sotiras A, Baller EB, Barzilay R, Calkins ME et al. 2020. Neurostructural heterogeneity in youths with internalizing symptoms. Biol. Psychiatry 87:5473–82
    [Google Scholar]
  101. Karcher NR, Barch DM. 2021. The ABCD study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology 46:1131–42
    [Google Scholar]
  102. Kelly AMC, Di Martino A, Uddin LQ, Shehzad Z, Gee DG et al. 2009. Development of anterior cingulate functional connectivity from late childhood to early adulthood. Cereb. Cortex 19:3640–57
    [Google Scholar]
  103. Kennedy DN, Abraham SA, Bates JF, Crowley A, Ghosh S et al. 2019. Everything matters: the ReproNim perspective on reproducible neuroimaging. Front. Neuroinform. 13:1
    [Google Scholar]
  104. Keshavan A, Yeatman JD, Rokem A. 2019. Combining citizen science and deep learning to amplify expertise in neuroimaging. Front. Neuroinform. 13:29
    [Google Scholar]
  105. Khalid A, Snyder JA. 2021. How to fix diversity and equity. The Chronicle of Higher Education May 27. https://www.chronicle.com/article/how-to-fix-diversity-and-equity
    [Google Scholar]
  106. Kharabian Masouleh S, Plachti A, Hoffstaedter F, Eickhoff S, Genon S 2020. Characterizing the gradients of structural covariance in the human hippocampus. NeuroImage 218:116972
    [Google Scholar]
  107. Knickmeyer RC, Gouttard S, Kang C, Evans D, Wilber K et al. 2008. A structural MRI study of human brain development from birth to 2 years. J. Neurosci. 28:4712176–82
    [Google Scholar]
  108. Kunkle BW, Schmidt M, Klein H-U, Naj AC, Hamilton-Nelson KL et al. 2021. Novel Alzheimer disease risk loci and pathways in African American individuals using the African genome resources panel: a meta-analysis. JAMA Neurol 78:1102–13
    [Google Scholar]
  109. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM et al. 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. PNAS 89:125675–79
    [Google Scholar]
  110. Lanka P, Deshpande G. 2019. Combining Prospective Acquisition CorrEction (PACE) with retrospective correction to reduce motion artifacts in resting state fMRI data. Brain Behav 9:8e01341
    [Google Scholar]
  111. Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ et al. 2015. Functional system and areal organization of a highly sampled individual human brain. Neuron 87:3657–70
    [Google Scholar]
  112. Laumann TO, Ortega M, Hoyt CR, Seider NA, Snyder AZ et al. 2021. Brain network reorganisation in an adolescent after bilateral perinatal strokes. Lancet Neurol 20:4255–56
    [Google Scholar]
  113. Lefebvre A, Beggiato A, Bourgeron T, Toro R. 2015. Neuroanatomical diversity of corpus callosum and brain volume in autism: meta-analysis, analysis of the Autism Brain Imaging Data Exchange Project, and simulation. Biol. Psychiatry 78:2126–34
    [Google Scholar]
  114. Liénard JF, Achakulvisut T, Acuna DE, David SV. 2018. Intellectual synthesis in mentorship determines success in academic careers. Nat. Commun. 9:14840
    [Google Scholar]
  115. Littlejohns TJ, Holliday J, Gibson LM, Garratt S, Oesingmann N et al. 2020. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11:12624
    [Google Scholar]
  116. Lyall AE, Shi F, Geng X, Woolson S, Li G et al. 2015. Dynamic development of regional cortical thickness and surface area in early childhood. Cereb. Cortex 25:82204–12
    [Google Scholar]
  117. Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM, Liston C. 2020. Rapid precision functional mapping of individuals using multi-echo fMRI. Cell Rep 33:12108540
    [Google Scholar]
  118. Marek S, Hwang K, Foran W, Hallquist MN, Luna B. 2015. The contribution of network organization and integration to the development of cognitive control. PLOS Biol 13:12e1002328
    [Google Scholar]
  119. Marek S, Siegel JS, Gordon EM, Raut RV, Gratton C et al. 2018. Spatial and temporal organization of the individual human cerebellum. Neuron 100:4977–93.e7
    [Google Scholar]
  120. Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP et al. 2020. Towards reproducible brain-wide association studies. bioRxiv 2020.08.21.257758. https://doi.org/10.1101/2020.08.21.257758
    [Crossref]
  121. Marek S, Tervo-Clemmens B, Nielsen AN, Wheelock MD, Miller RL et al. 2019. Identifying reproducible individual differences in childhood functional brain networks: an ABCD study. Dev. Cogn. Neurosci. 40:100706
    [Google Scholar]
  122. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:4584–91
    [Google Scholar]
  123. Maton KI, Beason TS, Godsay S, Sto Domingo MR, Bailey TC et al. 2016. Outcomes and processes in the Meyerhoff Scholars Program: STEM PhD completion, sense of community, perceived program benefit, science identity, and research self-efficacy. CBE Life Sci. Educ. 15:3 https://doi.org/10.1187/cbe.16-01-0062
    [Crossref] [Google Scholar]
  124. Medland SE, Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L et al. 2020. Ten years of enhancing neuro-imaging genetics through meta-analysis: an overview from the ENIGMA genetics working group. Hum. Brain Mapp press. https://doi.org/10.1002/hbm.25311
    [Crossref] [Google Scholar]
  125. Mennes M, Biswal BB, Castellanos FX, Milham MP. 2013. Making data sharing work: the FCP/INDI experience. NeuroImage 82:683–91
    [Google Scholar]
  126. Milham MP, Craddock RC, Son JJ, Fleischmann M, Clucas J et al. 2018. Assessment of the impact of shared brain imaging data on the scientific literature. Nat. Commun. 9:12818
    [Google Scholar]
  127. Milham MP, Vogelstein J, Xu T. 2021. Removing the reliability bottleneck in functional magnetic resonance imaging research to achieve clinical utility. JAMA Psychiatry 78:6587–88
    [Google Scholar]
  128. Miranda-Dominguez O, Mills BD, Carpenter SD, Grant KA, Kroenke CD et al. 2014. Connectotyping: model based fingerprinting of the functional connectome. PLOS ONE 9:11e111048
    [Google Scholar]
  129. Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J et al. 2010. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63:51144–53
    [Google Scholar]
  130. Morris JC, Schindler SE, McCue LM, Moulder KL, Benzinger TLS et al. 2019. Assessment of racial disparities in biomarkers for Alzheimer disease. JAMA Neurol 76:3264–73
    [Google Scholar]
  131. Naselaris T, Allen E, Kay K 2021. Extensive sampling for complete models of individual brains. Curr. Opin. Behav. Sci. 40:45–51
    [Google Scholar]
  132. Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM et al. 2012. The NKI-Rockland Sample: a model for accelerating the pace of discovery science in psychiatry. Front. Neurosci. 6:152
    [Google Scholar]
  133. O'Connor D, Potler NV, Kovacs M, Xu T, Ai L et al. 2017. The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions. GigaScience 6:2giw011
    [Google Scholar]
  134. Patel AX, Kundu P, Rubinov M, Jones PS, Vértes PE et al. 2014. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. NeuroImage 95:287–304
    [Google Scholar]
  135. Petersen SE, Fox PT, Posner MI, Mintun M, Raichle ME 1988. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature 331:6157585–89
    [Google Scholar]
  136. Petersen SE, Posner MI. 2012. The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35:73–89
    [Google Scholar]
  137. Piven J, Saliba K, Bailey J, Arndt S 1997. An MRI study of autism: the cerebellum revisited. Neurology 49:546–51
    [Google Scholar]
  138. Poldrack RA, Laumann TO, Koyejo O, Gregory B, Hover A et al. 2015. Long-term neural and physiological phenotyping of a single human. Nat. Commun. 6:8885
    [Google Scholar]
  139. Popkin G. 2019. Data sharing and how it can benefit your scientific career. Nature May 13. https://www.nature.com/articles/d41586-019-01506-x
    [Google Scholar]
  140. Posner MI, Petersen SE. 1990. The attention system of the human brain. Annu. Rev. Neurosci. 13:25–42
    [Google Scholar]
  141. Posner MI, Pothbart MK, Digirolamo GJ. 1999. Development of brain networks for orienting to novelty. Zhurnal Vysshei Nervn. Deiatelnosti Im. IP Pavlov. 49:5715–22
    [Google Scholar]
  142. Posner MI, Raichle ME. 1998. The neuroimaging of human brain function. PNAS 95:3763–64
    [Google Scholar]
  143. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59:32142–54
    [Google Scholar]
  144. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA et al. 2011. Functional network organization of the human brain. Neuron 72:4665–78
    [Google Scholar]
  145. Power JD, Fair DA, Schlaggar BL, Petersen SE. 2010. The development of human functional brain networks. Neuron 67:5735–48
    [Google Scholar]
  146. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. 2014. Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage 84:320–41
    [Google Scholar]
  147. Power JD, Schlaggar BL, Petersen SE. 2015. Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage 105:536–51
    [Google Scholar]
  148. Press G. 2020. Data scientists salaries and jobs immune to Covid-19, survey finds. Forbes Magazine Aug. 27. https://www.forbes.com/sites/gilpress/2020/08/27/data-scientists-salaries-and-jobs-immune-to-covid-19-survey-finds/
    [Google Scholar]
  149. PRIMatE Data Exch. (PRIME-DE) Glob. Collab. Workshop Consort 2020. Accelerating the evolution of nonhuman primate neuroimaging. Neuron 105:4600–3
    [Google Scholar]
  150. Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. 2015. ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage 112:267–77
    [Google Scholar]
  151. Raichle ME. 1998. Behind the scenes of functional brain imaging: a historical and physiological perspective. PNAS 95:3765–72
    [Google Scholar]
  152. Raichle ME. 2009. A brief history of human brain mapping. Trends Neurosci 32:2118–26
    [Google Scholar]
  153. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. 2001. A default mode of brain function. PNAS 98:2676–82
    [Google Scholar]
  154. Ramirez JSB, Graham AM, Thompson JR, Zhu JY, Sturgeon D et al. 2020. Maternal interleukin-6 is associated with macaque offspring amygdala development and behavior. Cereb. Cortex 30:1573–85
    [Google Scholar]
  155. Rapin I, Katzman R. 1998. Neurobiology of autism. Ann. Neurol. 43:17–14
    [Google Scholar]
  156. Raut RV, Mitra A, Marek S, Ortega M, Snyder AZ et al. 2020. Organization of propagated intrinsic brain activity in individual humans. Cereb. Cortex 30:31716–34
    [Google Scholar]
  157. Rodríguez-Sánchez F, Marwick B, Lazowska ED, VanderPlas J. 2017. Academia's failure to retain data scientists. Science 355:6323357–58
    [Google Scholar]
  158. Sabuncu MR, Konukoglu E. 2015. Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 13:131–46
    [Google Scholar]
  159. Sahneh F, Balk MA, Kisley M, Chan C-K, Fox M et al. 2021. Ten simple rules to cultivate transdisciplinary collaboration in data science. PLOS Comput. Biol. 17:5e1008879
    [Google Scholar]
  160. Sato JR, Salum GA, Gadelha A, Picon FA, Pan PM et al. 2014. Age effects on the default mode and control networks in typically developing children. J. Psychiatr. Res. 58:89–95
    [Google Scholar]
  161. Satterthwaite TD, Ciric R, Roalf DR, Davatzikos C, Bassett DS, Wolf DH. 2019. Motion artifact in studies of functional connectivity: characteristics and mitigation strategies. Hum. Brain Mapp. 40:72033–51
    [Google Scholar]
  162. Satterthwaite TD, Feczko E, Kaczkurkin AN, Fair DA. 2020. Parsing psychiatric heterogeneity through common and unique circuit-level deficits. Biol. Psychiatry 88:14–5
    [Google Scholar]
  163. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA et al. 2012. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. NeuroImage 60:1623–32
    [Google Scholar]
  164. Satterthwaite TD, Xia CH, Bassett DS. 2018. Personalized neuroscience: common and individual-specific features in functional brain networks. Neuron 98:243–45
    [Google Scholar]
  165. Schaeffer DJ, Hori Y, Gilbert KM, Gati JS, Menon RS, Everling S. 2020. Divergence of rodent and primate medial frontal cortex functional connectivity. PNAS 117:3521681–89
    [Google Scholar]
  166. Scott JA, Grayson D, Fletcher E, Lee A, Bauman MD et al. 2016. Longitudinal analysis of the developing rhesus monkey brain using magnetic resonance imaging: birth to adulthood. Brain Struct. Funct. 221:52847–71
    [Google Scholar]
  167. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH et al. 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27:92349–56
    [Google Scholar]
  168. Sherman LE, Rudie JD, Pfeifer JH, Masten CL, McNealy K, Dapretto M. 2014. Development of the default mode and central executive networks across early adolescence: a longitudinal study. Dev. Cogn. Neurosci. 10:148–59
    [Google Scholar]
  169. Siegel JS, Mitra A, Laumann TO, Seitzman BA, Raichle M et al. 2017. Data quality influences observed links between functional connectivity and behavior. Cereb. Cortex 27:94492–502
    [Google Scholar]
  170. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM et al. 2009. Correspondence of the brain's functional architecture during activation and rest. PNAS 106:3113040–45
    [Google Scholar]
  171. Smith SM, Nichols TE. 2018. Statistical challenges in ‘big data’ human neuroimaging. Neuron 97:2263–68
    [Google Scholar]
  172. Smyser CD, Inder TE, Shimony JS, Hill JE, Degnan AJ et al. 2010. Longitudinal analysis of neural network development in preterm infants. Cereb. Cortex 20:122852–62
    [Google Scholar]
  173. So long to the silos 2016. Nat. Biotechnol. 34:4357
  174. Sporns O, Tononi G, Kötter R. 2005. The human connectome: a structural description of the human brain. PLOS Comput. Biol. 1:4e42
    [Google Scholar]
  175. Stafford JM, Jarrett BR, Miranda-Dominguez O, Mills BD, Cain N et al. 2014. Large-scale topology and the default mode network in the mouse connectome. PNAS 111:5218745–50
    [Google Scholar]
  176. Stiles J, Jernigan TL. 2010. The basics of brain development. Neuropsychol. Rev. 20:4327–48
    [Google Scholar]
  177. Supekar K, Musen M, Menon V. 2009. Development of large-scale functional brain networks in children. PLOS Biol 7:7e1000157
    [Google Scholar]
  178. Supekar K, Uddin LQ, Prater K, Amin H, Greicius MD, Menon V. 2010. Development of functional and structural connectivity within the default mode network in young children. NeuroImage 52:1290–301
    [Google Scholar]
  179. Sylvester CM, Yu Q, Srivastava AB, Marek S, Zheng A et al. 2020. Individual-specific functional connectivity of the amygdala: a substrate for precision psychiatry. PNAS 117:73808–18
    [Google Scholar]
  180. Thomason ME, Dassanayake MT, Shen S, Katkuri Y, Alexis M et al. 2013. Cross-hemispheric functional connectivity in the human fetal brain. Sci. Transl. Med. 5:173173ra24
    [Google Scholar]
  181. Thompson PM, Jahanshad N, Ching CRK, Salminen LE, Thomopoulos SI et al. 2020. ENIGMA and global neuroscience: a decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl. Psychiatry 10:1100
    [Google Scholar]
  182. Tian Y, Zalesky A. 2021. Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?. bioRxiv 2021.05.27.446059. https://doi.org/10.1101/2021.05.27.446059
    [Crossref]
  183. Tisdall MD, Reuter M, Qureshi A, Buckner RL, Fischl B, van der Kouwe AJW. 2016. Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion. NeuroImage 127:11–22
    [Google Scholar]
  184. Uddin LQ. 2011. Resting-state fMRI and developmental systems neuroscience. Front. Neurosci. 5:14
    [Google Scholar]
  185. Uddin LQ, Supekar KS, Ryali S, Menon V 2011. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J. Neurosci. 31:5018578–89
    [Google Scholar]
  186. US Bur. Labor Stat 2021. Occupational employment and wages, May 2020: 15-1221 computer and information research scientists. US Bureau of Labor Statistics https://www.bls.gov/oes/current/oes151221.htm
    [Google Scholar]
  187. van den Heuvel MP, Kersbergen KJ, de Reus MA, Keunen K, Kahn RS et al. 2015. The neonatal connectome during preterm brain development. Cereb. Cortex 25:93000–13
    [Google Scholar]
  188. Van Dijk KRA, Sabuncu MR, Buckner RL. 2012. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59:1431–38
    [Google Scholar]
  189. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K 2013. The WU-Minn human connectome project: an overview. NeuroImage 80:62–79
    [Google Scholar]
  190. Van Horn JD, Gazzaniga MS. 2002. Databasing fMRI studies towards a ‘discovery science’ of brain function. Nat. Rev. Neurosci. 3:4314–18
    [Google Scholar]
  191. van Zwet E, Cator E. 2020. The significance filter, the winner's curse and the need to shrink. arXiv:2009.09440 [stat.ME]. http://arxiv.org/abs/2009.09440
  192. Vickers AJ. 2003. How many repeated measures in repeated measures designs? Statistical issues for comparative trials. BMC Med. Res. Methodol 3:22
    [Google Scholar]
  193. Volkow ND, Gordon JA, Freund MP 2020. The Healthy Brain and Child Development Study—shedding light on opioid exposure, COVID-19, and health disparities. JAMA Psychiatry 78:5741–72
    [Google Scholar]
  194. Volkow ND, Koob GF, Croyle RT, Bianchi DW, Gordon JA et al. 2018. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32:4–7
    [Google Scholar]
  195. Walum H, Waldman ID, Young LJ 2016. Statistical and methodological considerations for the interpretation of intranasal oxytocin studies. Biol. Psychiatry 79:3251–57
    [Google Scholar]
  196. Weng X-C, Zuo C-N. 2014. One-month test-retest reliability and dynamical resting-state study Consortium for Reliability and Reproducibility accessed June 7 2021. http://fcon_1000.projects.nitrc.org/indi/CoRR/html/hnu_1.html
  197. Xu T, Nenning K-H, Schwartz E, Hong S-J, Vogelstein JT et al. 2020. Cross-species functional alignment reveals evolutionary hierarchy within the connectome. NeuroImage 223:117346
    [Google Scholar]
  198. Yan C-G, Craddock RC, Zuo X-N, Zang Y-F, Milham MP. 2013. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage 80:246–62
    [Google Scholar]
  199. Yeo BTT, Krienen FM, Eickhoff SB, Yaakub SN, Fox PT et al. 2015. Functional specialization and flexibility in human association cortex. Cereb. Cortex 25:103654–72
    [Google Scholar]
  200. Zheng A, Montez DF, Marek S, Gilmore AW, Newbold DJ et al. 2020. Parallel hippocampal-parietal circuits for self- and goal-oriented processing. bioRxiv 2020.12.01.395210. https://www.biorxiv.org/content/10.1101/2020.12.01.395210
  201. Zuo X-N, Anderson JS, Bellec P, Birn RM, Biswal BB et al. 2014. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1:140049
    [Google Scholar]
  202. Zuo X-N, Biswal BB, Poldrack RA. 2019a. Editorial: reliability and reproducibility in functional connectomics. Front. Neurosci. 13:117
    [Google Scholar]
  203. Zuo X-N, Xu T, Milham MP. 2019b. Harnessing reliability for neuroscience research. Nat. Hum. Behav. 3:8768–71
    [Google Scholar]
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